{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UT</th>\n",
       "      <th>Author_Full_Names</th>\n",
       "      <th>Article_Title</th>\n",
       "      <th>Source_Title</th>\n",
       "      <th>Author_Keywords</th>\n",
       "      <th>Keywords_Plus</th>\n",
       "      <th>Abstract</th>\n",
       "      <th>Addresses</th>\n",
       "      <th>Publication_Date</th>\n",
       "      <th>Publication_Year</th>\n",
       "      <th>DOI</th>\n",
       "      <th>WoS_Categories</th>\n",
       "      <th>Research_Areas</th>\n",
       "      <th>一级分类</th>\n",
       "      <th>二级分类</th>\n",
       "      <th>Impact Factor</th>\n",
       "      <th>AnnualP</th>\n",
       "      <th>JRCzone</th>\n",
       "      <th>JCLZone</th>\n",
       "      <th>Quote_Factor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WOS:000330829200017</td>\n",
       "      <td>Kondor Daniel;Posfai Marton;Csabai Istvan;Vatt...</td>\n",
       "      <td>Do the Rich Get Richer? An Empirical Analysis ...</td>\n",
       "      <td>PLOS ONE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>STATISTICAL-MECHANICS; HEAVY TAILS; WEALTH; DI...</td>\n",
       "      <td>The possibility to analyze everyday monetary t...</td>\n",
       "      <td>[Kondor, Daniel; Posfai, Marton; Csabai, Istva...</td>\n",
       "      <td>2022-02-05 00:00:00</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>10.1371/journal.pone.0086197</td>\n",
       "      <td>Multidisciplinary Sciences</td>\n",
       "      <td>Science &amp; Technology - Other Topics</td>\n",
       "      <td>经济与管理主题</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.24</td>\n",
       "      <td>11244</td>\n",
       "      <td>Q2</td>\n",
       "      <td>Q1</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WOS:000341100800027</td>\n",
       "      <td>Garcia David;Tessone Claudio J.;Mavrodiev Pavl...</td>\n",
       "      <td>The digital traces of bubbles: feedback cycles...</td>\n",
       "      <td>JOURNAL OF THE ROYAL SOCIETY INTERFACE</td>\n",
       "      <td>social interactions; bubbles; socio-economic s...</td>\n",
       "      <td>SOCIAL-INFLUENCE</td>\n",
       "      <td>What is the role of social interactions in the...</td>\n",
       "      <td>[Garcia, David; Tessone, Claudio J.; Mavrodiev...</td>\n",
       "      <td>2022-10-06 00:00:00</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>10.1098/rsif.2014.0623</td>\n",
       "      <td>Multidisciplinary Sciences</td>\n",
       "      <td>Science &amp; Technology - Other Topics</td>\n",
       "      <td>应用主题</td>\n",
       "      <td>金融领域</td>\n",
       "      <td>4.118</td>\n",
       "      <td>279</td>\n",
       "      <td>Q2</td>\n",
       "      <td>Q1</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WOS:000346822900003</td>\n",
       "      <td>Kondor Daniel;Csabai Istvan;Szuele Janos;Posfa...</td>\n",
       "      <td>Inferring the interplay between network struct...</td>\n",
       "      <td>NEW JOURNAL OF PHYSICS</td>\n",
       "      <td>Bitcoin; transaction network; financial networ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A main focus in economics research is understa...</td>\n",
       "      <td>[Kondor, Daniel; Csabai, Istvan; Szuele, Janos...</td>\n",
       "      <td>2022-12-02 00:00:00</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>10.1088/1367-2630/16/12/125003</td>\n",
       "      <td>Physics, Multidisciplinary</td>\n",
       "      <td>Physics</td>\n",
       "      <td>经济与管理主题</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.729</td>\n",
       "      <td>626</td>\n",
       "      <td>Q1</td>\n",
       "      <td>Q1</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WOS:000349989300002</td>\n",
       "      <td>Cheung Adrian (Wai-Kong);Roca Eduardo;Su Jen-Je</td>\n",
       "      <td>Crypto-currency bubbles: an application of the...</td>\n",
       "      <td>APPLIED ECONOMICS</td>\n",
       "      <td>bitcoin; bubbles; crypto-currency</td>\n",
       "      <td>EXCHANGE-RATE</td>\n",
       "      <td>The creation of bitcoin heralded the arrival o...</td>\n",
       "      <td>[Cheung, Adrian (Wai-Kong)] Curtin Univ, Sch E...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>10.1080/00036846.2015.1005827</td>\n",
       "      <td>Economics</td>\n",
       "      <td>Business &amp; Economics</td>\n",
       "      <td>经济与管理主题</td>\n",
       "      <td>金融领域</td>\n",
       "      <td>1.835</td>\n",
       "      <td>420</td>\n",
       "      <td>Q3</td>\n",
       "      <td>Q2</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>WOS:000344596300006</td>\n",
       "      <td>Baek C.;Elbeck M.</td>\n",
       "      <td>Bitcoins as an investment or speculative vehic...</td>\n",
       "      <td>APPLIED ECONOMICS LETTERS</td>\n",
       "      <td>G14; G11; speculation; detrended ratio; invest...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>We use Bitcoin and S&amp;P 500 Index daily return ...</td>\n",
       "      <td>[Baek, C.; Elbeck, M.] Troy Univ, Sorrell Coll...</td>\n",
       "      <td>2022-01-02 00:00:00</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>10.1080/13504851.2014.916379</td>\n",
       "      <td>Economics</td>\n",
       "      <td>Business &amp; Economics</td>\n",
       "      <td>经济与管理主题</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.157</td>\n",
       "      <td>316</td>\n",
       "      <td>Q4</td>\n",
       "      <td>Q3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     UT                                  Author_Full_Names  \\\n",
       "id                                                                           \n",
       "1   WOS:000330829200017  Kondor Daniel;Posfai Marton;Csabai Istvan;Vatt...   \n",
       "2   WOS:000341100800027  Garcia David;Tessone Claudio J.;Mavrodiev Pavl...   \n",
       "3   WOS:000346822900003  Kondor Daniel;Csabai Istvan;Szuele Janos;Posfa...   \n",
       "4   WOS:000349989300002    Cheung Adrian (Wai-Kong);Roca Eduardo;Su Jen-Je   \n",
       "5   WOS:000344596300006                                  Baek C.;Elbeck M.   \n",
       "\n",
       "                                        Article_Title  \\\n",
       "id                                                      \n",
       "1   Do the Rich Get Richer? An Empirical Analysis ...   \n",
       "2   The digital traces of bubbles: feedback cycles...   \n",
       "3   Inferring the interplay between network struct...   \n",
       "4   Crypto-currency bubbles: an application of the...   \n",
       "5   Bitcoins as an investment or speculative vehic...   \n",
       "\n",
       "                              Source_Title  \\\n",
       "id                                           \n",
       "1                                 PLOS ONE   \n",
       "2   JOURNAL OF THE ROYAL SOCIETY INTERFACE   \n",
       "3                   NEW JOURNAL OF PHYSICS   \n",
       "4                        APPLIED ECONOMICS   \n",
       "5                APPLIED ECONOMICS LETTERS   \n",
       "\n",
       "                                      Author_Keywords  \\\n",
       "id                                                      \n",
       "1                                                 NaN   \n",
       "2   social interactions; bubbles; socio-economic s...   \n",
       "3   Bitcoin; transaction network; financial networ...   \n",
       "4                   bitcoin; bubbles; crypto-currency   \n",
       "5   G14; G11; speculation; detrended ratio; invest...   \n",
       "\n",
       "                                        Keywords_Plus  \\\n",
       "id                                                      \n",
       "1   STATISTICAL-MECHANICS; HEAVY TAILS; WEALTH; DI...   \n",
       "2                                    SOCIAL-INFLUENCE   \n",
       "3                                                 NaN   \n",
       "4                                       EXCHANGE-RATE   \n",
       "5                                                 NaN   \n",
       "\n",
       "                                             Abstract  \\\n",
       "id                                                      \n",
       "1   The possibility to analyze everyday monetary t...   \n",
       "2   What is the role of social interactions in the...   \n",
       "3   A main focus in economics research is understa...   \n",
       "4   The creation of bitcoin heralded the arrival o...   \n",
       "5   We use Bitcoin and S&P 500 Index daily return ...   \n",
       "\n",
       "                                            Addresses     Publication_Date  \\\n",
       "id                                                                           \n",
       "1   [Kondor, Daniel; Posfai, Marton; Csabai, Istva...  2022-02-05 00:00:00   \n",
       "2   [Garcia, David; Tessone, Claudio J.; Mavrodiev...  2022-10-06 00:00:00   \n",
       "3   [Kondor, Daniel; Csabai, Istvan; Szuele, Janos...  2022-12-02 00:00:00   \n",
       "4   [Cheung, Adrian (Wai-Kong)] Curtin Univ, Sch E...                  NaN   \n",
       "5   [Baek, C.; Elbeck, M.] Troy Univ, Sorrell Coll...  2022-01-02 00:00:00   \n",
       "\n",
       "    Publication_Year                             DOI  \\\n",
       "id                                                     \n",
       "1             2014.0    10.1371/journal.pone.0086197   \n",
       "2             2014.0          10.1098/rsif.2014.0623   \n",
       "3             2014.0  10.1088/1367-2630/16/12/125003   \n",
       "4             2015.0   10.1080/00036846.2015.1005827   \n",
       "5             2015.0    10.1080/13504851.2014.916379   \n",
       "\n",
       "                WoS_Categories                       Research_Areas     一级分类  \\\n",
       "id                                                                             \n",
       "1   Multidisciplinary Sciences  Science & Technology - Other Topics  经济与管理主题   \n",
       "2   Multidisciplinary Sciences  Science & Technology - Other Topics     应用主题   \n",
       "3   Physics, Multidisciplinary                              Physics  经济与管理主题   \n",
       "4                    Economics                 Business & Economics  经济与管理主题   \n",
       "5                    Economics                 Business & Economics  经济与管理主题   \n",
       "\n",
       "    二级分类 Impact Factor AnnualP JRCzone JCLZone Quote_Factor  \n",
       "id                                                           \n",
       "1    NaN          3.24   11244      Q2      Q1         0.57  \n",
       "2   金融领域         4.118     279      Q2      Q1         0.68  \n",
       "3    NaN         3.729     626      Q1      Q1         1.15  \n",
       "4   金融领域         1.835     420      Q3      Q2         0.68  \n",
       "5    NaN         1.157     316      Q4      Q3            0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "df = pd.read_excel(\"./信息组织-分类作业整理.xlsx\", sheet_name = \"Sheet6\")\n",
    "df.index = df['id']\n",
    "del df['id']\n",
    "df.head()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import calendar\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "import re\n",
    "import time\n",
    "\n",
    "df = pd.read_excel(\"./data.xlsx\", sheet_name = \"Sheet6\")\n",
    "# ============================ Initialize ============================\n",
    "# print(df.head() )\n",
    "writer_excel = pd.ExcelWriter(\"./output.xlsx\")\n",
    "\n",
    "# df_source_title = df.value_counts(['Source_Title'])\n",
    "# df_research_areas = df.value_counts(['Research_Areas'])\n",
    "# df_source_title.to_excel(writer_excel, sheet_name = 'source_title', index = None, header = True)\n",
    "# df_research_areas.to_excel(writer_excel, sheet_name = 'research_areas', index = None, header = True)\n",
    "# print(df_source_title)\n",
    "# ============================ Initialize ============================\n",
    "\n",
    "\n",
    "# ============================ Month Process ============================\n",
    "# publication_date = list(df['Publication_Date'] )\n",
    "# months = {\"jan\" : \"1\", \"feb\": \"2\" ,\"mar\":\"3\",\"apr\":\"4\",\"may\":\"5\", \"jun\": \"6\", \"jul\" : \"7\", \"aug\" : \"8\", \"sep\":\"9\", \"oct\" : \"10\", \"nov\" : \"11\", \"dec\" : \"12\"}\n",
    "# for idx, date_ in  enumerate(publication_date):\n",
    "#     if isinstance(date_, str):\n",
    "#         cur_m = date_[:3].lower()\n",
    "#         if cur_m not in months:\n",
    "#             publication_date[idx] = \"\"\n",
    "#         else:\n",
    "#             publication_date[idx] = months[cur_m]\n",
    "#         pass\n",
    "#     elif isinstance(date_, float):\n",
    "#         publication_date[idx] = \"\"\n",
    "#     else:\n",
    "#         publication_date[idx] = publication_date[idx].strftime(\"%m\").lstrip('0') \n",
    "# df['Publication_Date'] = publication_date\n",
    "\n",
    "# ============================ Month Process ============================\n",
    "\n",
    "\n",
    "# ============================ Author ============================\n",
    "\n",
    "# author = []\n",
    "# for idx, authors in enumerate(df['Author_Full_Names']):\n",
    "#     authors_list = authors.split(\";\")\n",
    "#     authors_list = list(map(lambda x : x.lower(), authors_list) )\n",
    "#     author = author + authors_list\n",
    "\n",
    "# author_counter = Counter(author)\n",
    "# authors_ = []\n",
    "# times_ = []\n",
    "# for auth in author_counter:\n",
    "#     authors_.append(auth)\n",
    "#     times_.append(author_counter[auth])\n",
    "# df_auth_times = pd.DataFrame({\"Author\" : authors_, \"Occurrence\" : times_})\n",
    "# df_auth_times.to_excel(writer_excel, sheet_name = 'Author_occurrence', index = None, header = True, encoding = 'utf8')\n",
    "# writer_excel.save()\n",
    "# print(sorted(author))\n",
    "# dt_authors = pd.DataFrame({\"Authors\" : author})\n",
    "# dt_authors.to_csv(\"./authors.csv\", index = None)\n",
    "\n",
    "\n",
    "# ============================ Author ============================\n",
    "# ============================ Time Split ============================\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                                     Acoustics\n",
      "1                  Automation & Control Systems\n",
      "2                              Computer Science\n",
      "3            Construction & Building Technology\n",
      "4                                Energy & Fuels\n",
      "5                                   Engineering\n",
      "6     Imaging Science & Photographic Technology\n",
      "7         Information Science & Library Science\n",
      "8                 Instruments & Instrumentation\n",
      "9                             Materials Science\n",
      "10                                    Mechanics\n",
      "11       Metallurgy & Metallurgical Engineering\n",
      "12                                   Microscopy\n",
      "13                 Nuclear Science & Technology\n",
      "14     Operations Research & Management Science\n",
      "15                               Remote Sensing\n",
      "16                                     Robotics\n",
      "17          Science & Technology - Other Topics\n",
      "18                                 Spectroscopy\n",
      "19                           Telecommunications\n",
      "20                               Transportation\n",
      "Name: Technology, dtype: object\n",
      "0              Agriculture\n",
      "1                  Allergy\n",
      "2     Anatomy & Morphology\n",
      "3           Anesthesiology\n",
      "4             Anthropology\n",
      "              ...         \n",
      "71       Tropical Medicine\n",
      "72    Urology & Nephrology\n",
      "73     Veterinary Sciences\n",
      "74                Virology\n",
      "75                 Zoology\n",
      "Name: Life Sciences & Biomedicine, Length: 76, dtype: object\n"
     ]
    }
   ],
   "source": [
    "import calendar\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "import re\n",
    "import time\n",
    "\n",
    "df = pd.read_excel(\"./data.xlsx\", sheet_name = \"Sheet6\")\n",
    "# ============================ Initialize ============================\n",
    "# print(df.head() )\n",
    "# writer_excel = pd.ExcelWriter(\"./output.xlsx\")\n",
    "\n",
    "# df_source_title = df.value_counts(['Source_Title'])\n",
    "# df_research_areas = df.value_counts(['Research_Areas'])\n",
    "# df_source_title.to_excel(writer_excel, sheet_name = 'source_title', index = None, header = True)\n",
    "# df_research_areas.to_excel(writer_excel, sheet_name = 'research_areas', index = None, header = True)\n",
    "# print(df_source_title)\n",
    "# ============================ Initialize ============================\n",
    "\n",
    "\n",
    "# ============================ Month Process ============================\n",
    "# publication_date = list(df['Publication_Date'] )\n",
    "# months = {\"jan\" : \"1\", \"feb\": \"2\" ,\"mar\":\"3\",\"apr\":\"4\",\"may\":\"5\", \"jun\": \"6\", \"jul\" : \"7\", \"aug\" : \"8\", \"sep\":\"9\", \"oct\" : \"10\", \"nov\" : \"11\", \"dec\" : \"12\"}\n",
    "# for idx, date_ in  enumerate(publication_date):\n",
    "#     if isinstance(date_, str):\n",
    "#         cur_m = date_[:3].lower()\n",
    "#         if cur_m not in months:\n",
    "#             publication_date[idx] = \"\"\n",
    "#         else:\n",
    "#             publication_date[idx] = months[cur_m]\n",
    "#         pass\n",
    "#     elif isinstance(date_, float):\n",
    "#         publication_date[idx] = \"\"\n",
    "#     else:\n",
    "#         publication_date[idx] = publication_date[idx].strftime(\"%m\").lstrip('0') \n",
    "# df['Publication_Date'] = publication_date\n",
    "\n",
    "# ============================ Month Process ============================\n",
    "\n",
    "\n",
    "# ============================ Author ============================\n",
    "\n",
    "# author = []\n",
    "# for idx, authors in enumerate(df['Author_Full_Names']):\n",
    "#     authors_list = authors.split(\";\")\n",
    "#     authors_list = list(map(lambda x : x.lower(), authors_list) )\n",
    "#     author = author + authors_list\n",
    "\n",
    "# author_counter = Counter(author)\n",
    "# authors_ = []\n",
    "# times_ = []\n",
    "# for auth in author_counter:\n",
    "#     authors_.append(auth)\n",
    "#     times_.append(author_counter[auth])\n",
    "# df_auth_times = pd.DataFrame({\"Author\" : authors_, \"Occurrence\" : times_})\n",
    "# df_auth_times.to_excel(writer_excel, sheet_name = 'Author_occurrence', index = None, header = True)\n",
    "# writer_excel.save()\n",
    "\n",
    "\n",
    "# print(sorted(author))\n",
    "# dt_authors = pd.DataFrame({\"Authors\" : author})\n",
    "# dt_authors.to_csv(\"./authors.csv\", index = None)\n",
    "\n",
    "\n",
    "# ============================ Author ============================\n",
    "\n",
    "# ============================ research areas ============================\n",
    "# 统计文章是否在多个涉及多个领域\n",
    "research_Areas = df['Research_Areas']\n",
    "# appear_times = list(map(lambda x : x.count(\";\"), research_Areas) )\n",
    "# result = Counter(appear_times)\n",
    "# print(result)\n",
    "df_research_areas = pd.read_excel(\"./output.xlsx\", sheet_name = \"Research_Areas\")\n",
    "ArtsHumanities = df_research_areas['Arts & Humanities'].dropna()\n",
    "medicine = df_research_areas[\"Life Sciences & Biomedicine\"].dropna()\n",
    "PhysicalSciences = df_research_areas[\"Physical Sciences\"].dropna()\n",
    "SocialSciences = df_research_areas[\"Social Sciences\"].dropna()\n",
    "Technology = df_research_areas[\"Technology\"].dropna()\n",
    "print(Technology)\n",
    "print(medicine)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Science & Technology - Other Topics': 85,\n",
       "         'Physics': 120,\n",
       "         'Business & Economics': 383,\n",
       "         'Computer Science': 1091,\n",
       "         'Engineering': 974,\n",
       "         'Telecommunications': 617,\n",
       "         'Mathematics': 47,\n",
       "         'Health Care Sciences & Services': 20,\n",
       "         'Medical Informatics': 22,\n",
       "         'Development Studies': 2,\n",
       "         'History & Philosophy of Science': 1,\n",
       "         'Mathematical Methods In Social Sciences': 26,\n",
       "         'Government & Law': 11,\n",
       "         'Energy & Fuels': 45,\n",
       "         'Environmental Sciences & Ecology': 65,\n",
       "         'Information Science & Library Science': 56,\n",
       "         'Automation & Control Systems': 87,\n",
       "         'Public Administration': 11,\n",
       "         'Mineralogy': 1,\n",
       "         'Biochemistry & Molecular Biology': 1,\n",
       "         'Biotechnology & Applied Microbiology': 5,\n",
       "         'Agriculture': 5,\n",
       "         'Food Science & Technology': 3,\n",
       "         'Construction & Building Technology': 24,\n",
       "         'Social Sciences - Other Topics': 10,\n",
       "         'Transportation': 56,\n",
       "         'Public, Environmental & Occupational Health': 2,\n",
       "         'Operations Research & Management Science': 66,\n",
       "         'Chemistry': 89,\n",
       "         'Instruments & Instrumentation': 68,\n",
       "         'Geography': 4,\n",
       "         'Social Issues': 4,\n",
       "         'Robotics': 4,\n",
       "         'Materials Science': 27,\n",
       "         'Mathematical & Computational Biology': 5,\n",
       "         'Thermodynamics': 5,\n",
       "         'Psychology': 8,\n",
       "         'Substance Abuse': 2,\n",
       "         'Sociology': 3,\n",
       "         'Criminology & Penology': 2,\n",
       "         'Education & Educational Research': 3,\n",
       "         'Cultural Studies': 1,\n",
       "         'Communication': 2,\n",
       "         'International Relations': 3,\n",
       "         'Genetics & Heredity': 4,\n",
       "         'Arts & Humanities - Other Topics': 2,\n",
       "         'Imaging Science & Photographic Technology': 1,\n",
       "         'Radiology, Nuclear Medicine & Medical Imaging': 1,\n",
       "         'Geology': 1,\n",
       "         'Neurosciences & Neurology': 1,\n",
       "         'Urban Studies': 1,\n",
       "         'Nuclear Science & Technology': 1,\n",
       "         'Optics': 3,\n",
       "         'Mechanics': 1,\n",
       "         'Spectroscopy': 1,\n",
       "         'General & Internal Medicine': 2,\n",
       "         'Electrochemistry': 1,\n",
       "         'Cardiovascular System & Cardiology': 1})"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = pd.read_excel(\"./data.xlsx\", sheet_name = \"Sheet6\")\n",
    "research_Areas = list(df['Research_Areas'] )\n",
    "total_research_areas = []\n",
    "for i in research_Areas:\n",
    "    if \";\" not in i:\n",
    "        total_research_areas.append(i)\n",
    "    else:\n",
    "        total_research_areas = total_research_areas + i.split(\"; \")\n",
    "tmp_res = Counter(total_research_areas)\n",
    "tmp_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(len(tmp_res))\n",
    "res_cat = []\n",
    "res_cat_cnt = []\n",
    "belongings = []\n",
    "\n",
    "ArtsHumanities = list(ArtsHumanities)\n",
    "medicine = list(medicine)\n",
    "PhysicalSciences = list(PhysicalSciences)\n",
    "SocialSciences = list(SocialSciences)\n",
    "Technology = list(Technology)\n",
    "\n",
    "for key, value in tmp_res.items():\n",
    "    key = key.strip()\n",
    "    if key in ArtsHumanities:\n",
    "        belongings.append(\"Arts & Humanities\")\n",
    "    elif key in medicine:\n",
    "        belongings.append(\"Life Sciences & Biomedicine\")\n",
    "    elif key in PhysicalSciences:\n",
    "        belongings.append(\"Physical Sciences\")\n",
    "    elif key in SocialSciences:\n",
    "        belongings.append(\"Social Sciences\")\n",
    "    elif key in Technology:\n",
    "        belongings.append(\"Technology\")\n",
    "    else:\n",
    "        print(\"Not in Types!\")\n",
    "        print(key)\n",
    "        # belongings.append()\n",
    "    res_cat.append(key)\n",
    "    res_cat_cnt.append(value)\n",
    "\n",
    "op_data = pd.DataFrame({\"Research Areas\" : res_cat, \"Research Areas Sum\": res_cat_cnt , \"Areas\" : belongings})\n",
    "op_data.to_excel(\"./test2.xlsx\", sheet_name = \"result1\")\n",
    "    # print(key, value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keywords_res_eli_empty = list(filter(lambda x : x[0] != '', keywords_res))\n",
    "# all_key_words = [i for t in keywords_res_eli_empty for i in t]\n",
    "# res_keywords = Counter(all_key_words)\n",
    "# print(res_keywords)\n",
    "\n",
    "# 产生作者共现\n",
    "import pandas as pd \n",
    "authors_ = list(df[\"Author_Full_Names\"])\n",
    "record_co_authors = set()\n",
    "res = dict()\n",
    "for i in authors_:\n",
    "    co_authors = i.split(\";\")\n",
    "    if len(co_authors) == 1:\n",
    "        continue\n",
    "    for idx in range(len(co_authors) - 1):\n",
    "        a_author = co_authors[idx].lower()\n",
    "        for j in range(idx + 1, len(co_authors)):\n",
    "            b_author = co_authors[j].lower()\n",
    "            if (a_author, b_author) in res:\n",
    "                res[(a_author, b_author)] += 1\n",
    "            elif (b_author, a_author) in res:\n",
    "                res[(b_author, a_author)] += 1\n",
    "            else:\n",
    "                res[(a_author, b_author)] = 1\n",
    "\n",
    "\n",
    "sources = []\n",
    "targets = []\n",
    "co = []\n",
    "for key, value in res.items():\n",
    "    sources.append(key[0])  \n",
    "    targets.append(key[1])\n",
    "    co.append(value)\n",
    "    sources.append(key[1])  \n",
    "    targets.append(key[0])\n",
    "    co.append(value)\n",
    "\n",
    "\n",
    "df = pd.DataFrame({\"Source\": sources, \"target\" : targets, \"Count\" : co})\n",
    "df.to_excel(\"text4.xlsx\", sheet_name=\"Test\")\n",
    "\n",
    "                \n",
    "\n",
    "            \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "d = {(1,2) : \"hello\", (2,1): \"world\"}\n",
    "print((1,2 ) in d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({3: 430, 2: 390, 4: 363, 1: 304, 5: 261, 0: 148, 6: 92, 7: 37, 8: 18, 11: 2, 9: 2, 14: 1, 64: 1, 12: 1})\n"
     ]
    }
   ],
   "source": [
    "# 810是只有1个方向的；483篇文章有2个方向；717篇文章有3个方向，40篇文章有4个方向。\n",
    "from functools import reduce\n",
    "count_author_independent = list(map(lambda x : x.count(\";\"), list(df[\"Author_Full_Names\"])))\n",
    "# print(count_author_independent)\n",
    "# print(len(count_author_independent))\n",
    "res_count_ind_authors = Counter(count_author_independent)\n",
    "print(res_count_ind_authors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "wos_categories = df['WoS_Categories']\n",
    "\n",
    "total_wos_categories = []\n",
    "for i in wos_categories:\n",
    "    if \";\" not in i:\n",
    "        total_wos_categories.append(i)\n",
    "    else:\n",
    "        total_wos_categories = total_wos_categories + i.split(\"; \")\n",
    "tmp_res_wos = Counter(total_wos_categories)\n",
    "# print(tmp_res_wos )\n",
    "ot_key = []\n",
    "ot_value = []\n",
    "for key, value in tmp_res_wos.items():\n",
    "    ot_key.append(key)\n",
    "    ot_value.append(value)\n",
    "ot_data = pd.DataFrame({\"Wos Categories\" : ot_key, \"Wos Categories count\" : ot_value})\n",
    "ot_data.to_excel(\"./test3.xlsx\", index = None, sheet_name = \"wos_cat\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IOT:69\n",
      "AI_Algorithm:84\n",
      "ENERGY:90\n",
      "DeCentral:52\n",
      "Contracts:33\n",
      "Computing:45\n",
      "Eth:11\n",
      "SupplyChain:61\n",
      "Security:100\n",
      "Privacy:46\n",
      "Authentication:52\n",
      "Crypto:95\n",
      "Data:142\n",
      "Architecture:19\n",
      "BlockChain:90\n",
      "BitCoin:36\n",
      "Systems:146\n",
      "Economy:48\n",
      "HealthCare:65\n",
      "1284\n",
      "7226\n"
     ]
    }
   ],
   "source": [
    "# 这段代码生成了一个“属性词表”\n",
    "# 一个小思路：想一下如何制定一个比较好的分类方法：\n",
    "# 比如iot就可以专门列入物联网部分\n",
    "# 比如AI的算法也可以单独分类进去\n",
    "# 比如“去中心化的”\n",
    "# 比如能源控制的部分\n",
    "# 比如contrac\n",
    "IOT = []\n",
    "AI_Algorithm = []\n",
    "ENERGY = []\n",
    "DeCentral = []\n",
    "Contracts = []\n",
    "Crypto = []\n",
    "Computing = []\n",
    "Eth = []\n",
    "SupplyChain = []\n",
    "Security = []\n",
    "Privacy = []\n",
    "Authentication = []\n",
    "Data = []\n",
    "BlockChain = []\n",
    "BitCoin = []\n",
    "Systems = []\n",
    "Architecture = []\n",
    "Economy = []\n",
    "HealthCare = []\n",
    "\n",
    "temp = pd.read_excel(\"./output.xlsx\", sheet_name=\"keywords_data\")\n",
    "ta = list(temp[\"Keywords\"])\n",
    "scale = list(temp[\"Times\"])\n",
    "\n",
    "total_words_count = 0\n",
    "for idx, a in enumerate(ta ):\n",
    "    if \"thing\" in a and \"of\" in a and \"internet\" in a or \"iot\" in a:\n",
    "        IOT.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"learning\" in a or \"algorithm\" in a:\n",
    "        AI_Algorithm.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"energy\" in a:\n",
    "        ENERGY.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"ethe\" in a:\n",
    "        Eth.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"decentral\" in a:\n",
    "        DeCentral.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"contract\" in a:\n",
    "        Contracts.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"computing\" in a:\n",
    "        Computing.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"supply chain\" in a:\n",
    "        SupplyChain.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"secur\" in a:\n",
    "        Security.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"private\" in a or \"privacy\" in a:\n",
    "        Privacy.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"authent\" in a or \"access\" in a and \"control\" in a:\n",
    "        Authentication.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"crypt\" in a:\n",
    "        Crypto.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"data\" in a:\n",
    "        Data.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"architect\" in a:\n",
    "        Architecture.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"econ\" in a:\n",
    "        Economy.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"health\" in a:\n",
    "        HealthCare.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"bitcoin\" in a or \"covid\" in a:\n",
    "        BitCoin.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"blockchain\" in a or \"block\" in a and \"chain\" in a:\n",
    "        BlockChain.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "    elif \"system\" in a:\n",
    "        Systems.append(a)\n",
    "        total_words_count += scale[idx]\n",
    "\n",
    "\n",
    "print(\"IOT:{}\".format(str(len(IOT))))\n",
    "print(\"AI_Algorithm:{}\".format(str(len(AI_Algorithm))))\n",
    "print(\"ENERGY:{}\".format(str(len(ENERGY))))\n",
    "print(\"DeCentral:{}\".format(str(len(DeCentral))))\n",
    "print(\"Contracts:{}\".format(str(len(Contracts))))\n",
    "print(\"Computing:{}\".format(str(len(Computing))))\n",
    "print(\"Eth:{}\".format(str(len(Eth))))\n",
    "print(\"SupplyChain:{}\".format(str(len(SupplyChain))))\n",
    "print(\"Security:{}\".format(str(len(Security))))\n",
    "print(\"Privacy:{}\".format(str(len(Privacy))))\n",
    "print(\"Authentication:{}\".format(str(len(Authentication))))\n",
    "print(\"Crypto:{}\".format(str(len(Crypto))))\n",
    "print(\"Data:{}\".format(str(len(Data))))\n",
    "print(\"Architecture:{}\".format(str(len(Architecture))))\n",
    "# print()\n",
    "print(\"BlockChain:{}\".format(str(len(BlockChain))))\n",
    "print(\"BitCoin:{}\".format(str(len(BitCoin))))\n",
    "print(\"Systems:{}\".format(str(len(Systems))))\n",
    "print(\"Economy:{}\".format(str(len(Economy))))\n",
    "print(\"HealthCare:{}\".format(str(len(HealthCare))))\n",
    "\n",
    "\n",
    "print(69 + 84 + 90 + 52 + 33 + 45 + 11 + 61 + 100 + 46 + 52 + 95 + 142 +19+ 90 + 36 + 146 + 48 + 65 )\n",
    "print(total_words_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'data': 145, 'management': 105, 'network': 103, 'system': 94, 'of': 92, 'blockchain': 87, 'energy': 83, 'and': 83, 'analysis': 77, 'model': 76, 'security': 69, 'smart': 69, 'systems': 68, 'chain': 68, 'supply': 64, 'distributed': 63, 'market': 62, 'information': 58, 'digital': 58, 'networks': 57, 'decentralized': 50, 'trading': 47, 'internet': 46, 'technology': 46, 'learning': 44, 'authentication': 42, 'computing': 40, 'health': 38, 'privacy': 36, 'sharing': 35, 'control': 34, 'service': 34, 'power': 34, 'volatility': 33, 'mobile': 32, 'iot': 31, 'communication': 31, 'signature': 31, 'financial': 30, 'electronic': 29, 'models': 28, 'key': 28, 'social': 28, 'things': 27, 'secure': 27, 'exchange': 27, 'detection': 27, 'bitcoin': 25, 'edge': 25, 'scheme': 25, 'markets': 25, 'healthcare': 25, 'vehicular': 25, 'encryption': 24, 'performance': 24, 'industry': 24, 'business': 24, 'storage': 23, 'vehicle': 23, 'trust': 22, 'cloud': 22, 'access': 22, 'medical': 22, 'risk': 22, 'theory': 22, 'proof': 22, 'optimization': 21, 'algorithm': 21, 'industrial': 21, 'services': 21, 'price': 21, 'vehicles': 21, 'demand': 21, 'wireless': 21, 'attack': 21, 'game': 20, 'innovation': 20, 'software': 20, 'mining': 20, 'consensus': 19, 'protocol': 19, 'food': 19, 'quantum': 19, 'contract': 18, 'mechanism': 18, 'adoption': 18, 'governance': 18, 'public': 18, 'construction': 18, 'applications': 18, 'architecture': 17, 'design': 17, 'transaction': 17, 'online': 17, 'sustainable': 17, 'machine': 16, 'graph': 16, 'asymmetric': 16, 'value': 16, 'development': 16, 'distribution': 16, 'peer-to-peer': 15, 'cryptography': 15, 'traceability': 15, 'resource': 15, 'attacks': 15, 'monitoring': 15, 'policy': 15, 'intelligent': 15, 'platform': 15, 'verification': 15, 'manufacturing': 15, 'uncertainty': 14, 'sensor': 14, 'records': 14, 'block': 14, 'payment': 14, 'contracts': 13, 'framework': 13, 'modeling': 13, 'grid': 13, '5g': 13, 'quality': 13, 'behavior': 13, 'generation': 13, 'economy': 13, 'strategy': 13, 'identity': 13, 'index': 13, 'to': 13, 'process': 13, 'economics': 12, 'ledger': 12, 'returns': 12, 'efficiency': 12, 'deep': 12, 'technologies': 12, 'research': 12, 'transparency': 12, 'user': 12, 'causality': 12, 'collaborative': 12, 'time': 12, 'signatures': 12, 'electricity': 12, 'education': 12, 'hash': 12, 'neural': 12, 'dynamic': 12, 'task': 11, 'computer': 11, 'incentive': 11, 'protection': 11, 'sensors': 11, 'selection': 11, 'reputation': 11, 'analytics': 11, 'currency': 11, 'knowledge': 11, 'token': 11, 'private': 11, 'virtual': 11, 'spillover': 11, 'asset': 11, 'search': 11, 'communications': 11, 'carbon': 11, 'law': 11, 'adaptive': 11, 'transactions': 11, 'node': 11, 'local': 11, 'processing': 11, 'charging': 11, 'approach': 11, 'community': 11, 'cryptocurrency': 10, 'challenges': 10, 'pricing': 10, 'integrity': 10, 'money': 10, 'networking': 10, 'auction': 10, 'stock': 10, 'economic': 10, 'lightweight': 10, 'routing': 10, 'agreement': 10, 'entropy': 10, 'regulation': 10, 'transfer': 10, 'wavelet': 10, 'cost': 10, 'decision': 10, 'frequency': 10, 'green': 10, 'crypto': 10, 'conditional': 10, 'code': 10, 'method': 10, 'in': 10, 'intelligence': 9, 'fog': 9, 'investment': 9, 'connectedness': 9, 'response': 9, 'agriculture': 9, 'computation': 9, 'electric': 9, 'return': 9, 'sentiment': 9, 'care': 9, 'radio': 9, 'algorithms': 9, 'stochastic': 9, 'aerial': 9, 'clustering': 9, 'study': 9, 'portfolio': 9, 'transportation': 9, 'support': 9, 'application': 9, 'byzantine': 9, 'engineering': 9, 'autonomous': 9, 'operations': 9, 'fuzzy': 9, 'production': 9, 'tree': 9, 'platforms': 9, 'estimation': 9, 'products': 9, 'content': 9, 'spectrum': 9, 'product': 9, 'recognition': 9, 'scheduling': 9, '(iot)': 8, 'big': 8, 'integration': 8, 'chains': 8, 'media': 8, 'offloading': 8, 'acceptance': 8, 'resources': 8, 'hypothesis': 8, 'science': 8, 'unmanned': 8, 'cyber': 8, 'connected': 8, 'devices': 8, 'quantile': 8, 'certificate': 8, 'flow': 8, 'traffic': 8, 'device': 8, 'bayesian': 8, 'banking': 8, 'sensing': 8, 'strategic': 8, 'methods': 8, 'time-varying': 8, 'for': 8, 'ethereum': 7, 'gold': 7, 'efficient': 7, 'covid-19': 7, 'crowdsourcing': 7, 'intrusion': 7, 'spillovers': 7, 'garch': 7, 'reinforcement': 7, 'dynamics': 7, 'attribute-based': 7, 'dependence': 7, 'strategies': 7, 'load': 7, 'currencies': 7, 'peer': 7, 'uav': 7, 'environment': 7, 'infrastructure': 7, 'aggregation': 7, 'ad': 7, 'fault': 7, 'perceived': 7, 'vector': 7, '(p2p)': 7, 'personal': 7, 'insurance': 7, 'logic': 7, 'state': 7, 'random': 7, 'sector': 7, 'predictive': 7, 'news': 7, 'variance': 7, 'identification': 7, 'structural': 7, 'granger': 7, 'markov': 7, 'event': 7, 'transmission': 7, 'anonymous': 7, 'use': 7, 'layer': 7, 'nodes': 7, 'fair': 7, 'hierarchical': 7, 'effects': 7, 'correlation': 7, 'image': 7, 'group': 7, 'assessment': 7, 'tourism': 7, 'optical': 7, 'blockchain-based': 7, 'optimal': 7, 'effect': 7, 'enterprise': 7, 'hyperledger': 6, 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'discrimination': 1, 'abnormal': 1, 'witching': 1, 'workload': 1, 'wool': 1, 'imperialism': 1, 'empire': 1, 'colonialism': 1, 'fiji': 1, 'cyber-libertarianism': 1, 'vanuatu': 1})\n"
     ]
    }
   ],
   "source": [
    "# 这里给出的是所有“单个词语”的频率\n",
    "all_words = []\n",
    "for i in ta:\n",
    "    all_words = all_words + i.split(\" \")\n",
    "print(Counter(all_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1941\n"
     ]
    }
   ],
   "source": [
    "# IOT = []\n",
    "# AI_Algorithm = []\n",
    "# ENERGY = []\n",
    "# DeCentral = []\n",
    "# Contracts = []\n",
    "# Crypto = []\n",
    "# Computing = []\n",
    "# Eth = []\n",
    "# SupplyChain = []\n",
    "# Security = []\n",
    "# Privacy = []\n",
    "# Authentication = []\n",
    "# Data = []\n",
    "# BlockChain = []\n",
    "# BitCoin = []\n",
    "# Systems = []\n",
    "# Architecture = []\n",
    "# Economy = []\n",
    "# HealthCare = []\n",
    "keywords_res = []\n",
    "for (a, b) in zip(list(df['Author_Keywords']), list(df[\"Keywords_Plus\"])):\n",
    "    tmp = \"\"\n",
    "    if isinstance(a, float):\n",
    "        tmp = tmp + \"\"\n",
    "    else:\n",
    "        tmp = tmp + a.lower()\n",
    "    if isinstance(b, float):\n",
    "        \n",
    "        tmp = tmp\n",
    "    else:\n",
    "        if len(tmp) > 0:\n",
    "            tmp = tmp + \"; \" + b.lower()\n",
    "        \n",
    "        else:\n",
    "            tmp = b.lower()\n",
    "    keywords_res.append(tmp)\n",
    "# print(len( keywords_res) )\n",
    "keytags = []\n",
    "t = 0\n",
    "for i in range(len(keywords_res)):\n",
    "    keywords_res[i] = list(set(keywords_res[i].split(\"; \")))\n",
    "for i in range(len(keywords_res)):\n",
    "    temp_tags = []\n",
    "    for j in range(len(keywords_res[i])):\n",
    "        if keywords_res[i][j] in IOT:\n",
    "            if \"IOT\" not in temp_tags:\n",
    "                temp_tags.append(\"IOT\")\n",
    "        if keywords_res[i][j] in AI_Algorithm:\n",
    "            if \"AI_Algorithm\" not in temp_tags:\n",
    "                temp_tags.append(\"AI_Algorithm\")\n",
    "        if keywords_res[i][j] in ENERGY:\n",
    "            if \"ENERGY\" not in temp_tags:\n",
    "                temp_tags.append(\"ENERGY\")\n",
    "        if keywords_res[i][j] in DeCentral:\n",
    "            if \"DeCentral\" not in temp_tags:\n",
    "                temp_tags.append(\"DeCentral\")\n",
    "        if keywords_res[i][j] in Contracts:\n",
    "            if \"Contracts\" not in temp_tags:\n",
    "                temp_tags.append(\"Contracts\")\n",
    "        if keywords_res[i][j] in Crypto:\n",
    "            if \"Crypto\" not in temp_tags:\n",
    "                temp_tags.append(\"Crypto\")\n",
    "        if keywords_res[i][j] in Computing:\n",
    "            if \"Computing\" not in temp_tags:\n",
    "                temp_tags.append(\"Computing\")\n",
    "        if keywords_res[i][j] in Eth:\n",
    "            if \"Eth\" not in temp_tags:\n",
    "                temp_tags.append(\"Eth\")\n",
    "        if keywords_res[i][j] in SupplyChain:\n",
    "            if \"SupplyChain\" not in temp_tags:\n",
    "                temp_tags.append(\"SupplyChain\")\n",
    "        if keywords_res[i][j] in Security:\n",
    "            if \"Security\" not in temp_tags:\n",
    "                temp_tags.append(\"Security\")\n",
    "        if keywords_res[i][j] in Privacy:\n",
    "            if \"Privacy\" not in temp_tags:\n",
    "                temp_tags.append(\"Privacy\")\n",
    "        if keywords_res[i][j] in Authentication:\n",
    "            if \"Authentication\" not in temp_tags:\n",
    "                temp_tags.append(\"Authentication\")\n",
    "        if keywords_res[i][j] in Data:\n",
    "            if \"Data\" not in temp_tags:\n",
    "                temp_tags.append(\"Data\")\n",
    "        if keywords_res[i][j] in BlockChain:\n",
    "            if \"BlockChain\" not in temp_tags:\n",
    "                temp_tags.append(\"BlockChain\")\n",
    "        if keywords_res[i][j] in BitCoin:\n",
    "            if \"BitCoin\" not in temp_tags:\n",
    "                temp_tags.append(\"BitCoin\")\n",
    "        if keywords_res[i][j] in Systems:\n",
    "            if \"Systems\" not in temp_tags:\n",
    "                temp_tags.append(\"Systems\")\n",
    "        if keywords_res[i][j] in Architecture:\n",
    "            if \"Architecture\" not in temp_tags:\n",
    "                temp_tags.append(\"Architecture\")\n",
    "        if keywords_res[i][j] in Economy:\n",
    "            if \"Economy\" not in temp_tags:\n",
    "                temp_tags.append(\"Economy\")\n",
    "        if keywords_res[i][j] in HealthCare:\n",
    "            if \"HealthCare\" not in temp_tags:\n",
    "                temp_tags.append(\"HealthCare\")\n",
    "    keytags.append(\",\".join(temp_tags))\n",
    "\n",
    "keytags = list(filter(lambda x : len(x) > 1, keytags))\n",
    "print(len(keytags))\n",
    "\n",
    "keytags = \"//\".join(keytags)\n",
    "# print(len(list(filter(lambda x : len(x) < 1, keytags))))\n",
    "# 只有109篇文献没有覆盖到（包括56篇压根就没有关键词的文献），效果很棒\n",
    "\n",
    "    # print(length)\n",
    "    # print(unique_length)\n",
    "# unique_key_words = list(map(lambda x : set(x.split(\"; \")), keywords_res))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                BitCoin  Economy  Crypto  Privacy  BlockChain  AI_Algorithm  \\\n",
      "BitCoin             1.0     67.0   229.0     27.0       120.0          21.0   \n",
      "Economy            67.0      1.0    48.0      5.0        53.0           5.0   \n",
      "Crypto            229.0     48.0     1.0     61.0       196.0          30.0   \n",
      "Privacy            27.0      5.0    61.0      1.0       306.0          53.0   \n",
      "BlockChain        120.0     53.0   196.0    306.0         1.0         196.0   \n",
      "AI_Algorithm       21.0      5.0    30.0     53.0       196.0           1.0   \n",
      "HealthCare         10.0      2.0    15.0     35.0       108.0          10.0   \n",
      "Eth                35.0      7.0    33.0      6.0        88.0           5.0   \n",
      "IOT                12.0      2.0    40.0    110.0       385.0          75.0   \n",
      "Systems            26.0     12.0    42.0     60.0       280.0          48.0   \n",
      "Data               26.0      7.0    41.0     84.0       253.0          45.0   \n",
      "Security           40.0      5.0    67.0    181.0       481.0          76.0   \n",
      "Authentication      5.0      0.0    35.0     79.0       189.0          19.0   \n",
      "Computing          16.0      3.0    30.0     71.0       249.0          52.0   \n",
      "Contracts          26.0     10.0    50.0     55.0       348.0          28.0   \n",
      "ENERGY             19.0     10.0    25.0     27.0       141.0          13.0   \n",
      "DeCentral          10.0      1.0    13.0     21.0        93.0           9.0   \n",
      "SupplyChain         5.0      2.0     5.0      6.0       132.0           7.0   \n",
      "Architecture        9.0      1.0    11.0     35.0       144.0          24.0   \n",
      "\n",
      "                HealthCare   Eth    IOT  Systems   Data  Security  \\\n",
      "BitCoin               10.0  35.0   12.0     26.0   26.0      40.0   \n",
      "Economy                2.0   7.0    2.0     12.0    7.0       5.0   \n",
      "Crypto                15.0  33.0   40.0     42.0   41.0      67.0   \n",
      "Privacy               35.0   6.0  110.0     60.0   84.0     181.0   \n",
      "BlockChain           108.0  88.0  385.0    280.0  253.0     481.0   \n",
      "AI_Algorithm          10.0   5.0   75.0     48.0   45.0      76.0   \n",
      "HealthCare             1.0   9.0   33.0     18.0   23.0      48.0   \n",
      "Eth                    9.0   1.0   18.0     19.0   14.0      28.0   \n",
      "IOT                   33.0  18.0    1.0     88.0   81.0     209.0   \n",
      "Systems               18.0  19.0   88.0      1.0   57.0     105.0   \n",
      "Data                  23.0  14.0   81.0     57.0    1.0     104.0   \n",
      "Security              48.0  28.0  209.0    105.0  104.0       1.0   \n",
      "Authentication        19.0  11.0   94.0     31.0   47.0     113.0   \n",
      "Computing             19.0   6.0  112.0     55.0   54.0     108.0   \n",
      "Contracts             29.0  70.0   70.0     73.0   58.0     112.0   \n",
      "ENERGY                 3.0   4.0   29.0     43.0   16.0      44.0   \n",
      "DeCentral             11.0   9.0   18.0     18.0   21.0      34.0   \n",
      "SupplyChain            6.0   9.0   27.0     45.0   26.0      26.0   \n",
      "Architecture          12.0   2.0   81.0     30.0   36.0      80.0   \n",
      "\n",
      "                Authentication  Computing  Contracts  ENERGY  DeCentral  \\\n",
      "BitCoin                    5.0       16.0       26.0    19.0       10.0   \n",
      "Economy                    0.0        3.0       10.0    10.0        1.0   \n",
      "Crypto                    35.0       30.0       50.0    25.0       13.0   \n",
      "Privacy                   79.0       71.0       55.0    27.0       21.0   \n",
      "BlockChain               189.0      249.0      348.0   141.0       93.0   \n",
      "AI_Algorithm              19.0       52.0       28.0    13.0        9.0   \n",
      "HealthCare                19.0       19.0       29.0     3.0       11.0   \n",
      "Eth                       11.0        6.0       70.0     4.0        9.0   \n",
      "IOT                       94.0      112.0       70.0    29.0       18.0   \n",
      "Systems                   31.0       55.0       73.0    43.0       18.0   \n",
      "Data                      47.0       54.0       58.0    16.0       21.0   \n",
      "Security                 113.0      108.0      112.0    44.0       34.0   \n",
      "Authentication             1.0       42.0       40.0     9.0       13.0   \n",
      "Computing                 42.0        1.0       56.0    30.0       15.0   \n",
      "Contracts                 40.0       56.0        1.0    38.0       42.0   \n",
      "ENERGY                     9.0       30.0       38.0     1.0       12.0   \n",
      "DeCentral                 13.0       15.0       42.0    12.0        1.0   \n",
      "SupplyChain                7.0        6.0       34.0     5.0        5.0   \n",
      "Architecture              32.0       44.0       27.0     7.0       12.0   \n",
      "\n",
      "                SupplyChain  Architecture  \n",
      "BitCoin                 5.0           9.0  \n",
      "Economy                 2.0           1.0  \n",
      "Crypto                  5.0          11.0  \n",
      "Privacy                 6.0          35.0  \n",
      "BlockChain            132.0         144.0  \n",
      "AI_Algorithm            7.0          24.0  \n",
      "HealthCare              6.0          12.0  \n",
      "Eth                     9.0           2.0  \n",
      "IOT                    27.0          81.0  \n",
      "Systems                45.0          30.0  \n",
      "Data                   26.0          36.0  \n",
      "Security               26.0          80.0  \n",
      "Authentication          7.0          32.0  \n",
      "Computing               6.0          44.0  \n",
      "Contracts              34.0          27.0  \n",
      "ENERGY                  5.0           7.0  \n",
      "DeCentral               5.0          12.0  \n",
      "SupplyChain             1.0          11.0  \n",
      "Architecture           11.0           1.0  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 很二，不要跑这个代码\n",
    "def authors_stat(co_authors_list):\n",
    "    au_dict = {}  # 单个作者频次统计\n",
    "    au_group = {}  # 两两作者合作\n",
    "    for authors in co_authors_list:\n",
    "        authors = authors.split(',')  # 按照逗号分开每个作者\n",
    "        authors_co = authors  # 合作者同样构建一个样本\n",
    "        for au in authors:\n",
    "            # 统计单个作者出现的频次\n",
    "            if au not in au_dict:\n",
    "                au_dict[au] = 1\n",
    "            else:\n",
    "                au_dict[au] += 1\n",
    "            # 统计合作的频次\n",
    "            authors_co = authors_co[1:]  # 去掉当前作者\n",
    "            for au_c in authors_co:\n",
    "                A, B = au, au_c  # 不能用本来的名字，否则会改变au自身\n",
    "                if A > B:\n",
    "                    A, B = B, A  # 保持两个作者名字顺序一致\n",
    "                co_au = A+','+B  # 将两个作者合并起来，依然以逗号隔开\n",
    "                if co_au not in au_group:\n",
    "                    au_group[co_au] = 1\n",
    "                else:\n",
    "                    au_group[co_au] += 1\n",
    "    return au_group, au_dict\n",
    "\n",
    "\n",
    "def generate_matrix(au_group, matrix):\n",
    "    for key, value in au_group.items():\n",
    "        if len(key.split(',') ) >= 2:\n",
    "            A = key.split(',')[0]\n",
    "            B = key.split(',')[1]\n",
    "            Fi = au_dict[A]\n",
    "            Fj = au_dict[B]\n",
    "            Eij = value\n",
    "            # Eij = value*value/(Fi*Fj)\n",
    "            # 按照作者进行索引，更新矩阵\n",
    "            matrix.loc[A, B] = Eij\n",
    "            matrix.loc[B, A] = Eij\n",
    "    return matrix\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "\n",
    "    co_authors = keytags\n",
    "    # co_authors = '张三,里斯,和,sd//和,徐徐,里斯,有,sd//有,和,星,b,sd'\n",
    "    # co_authors = 'a,b,c//a,c,d//b,c,d'\n",
    "    # authors = list(df[\"Author_Full_Names\"] ) \n",
    "    # authors = list(map(lambda x : x.lower(), authors))\n",
    "    # co_authors = \"//\".join(authors)\n",
    "    co_authors_list = co_authors.split('//')\n",
    "    au_group, au_dict = authors_stat(co_authors_list)\n",
    "    # print(au_group)\n",
    "    # print(au_dict)\n",
    "    au_list = list(au_dict.keys())  # 取出所有单个作者\n",
    "    # 新建一个空矩阵\n",
    "    matrix = pd.DataFrame(np.identity(len(au_list)), columns=au_list, index=au_list)\n",
    "    # print(matrix)\n",
    "    matrix = generate_matrix(au_group, matrix)\n",
    "    print(matrix)\n",
    "    matrix.to_csv(\"./test3.csv\", index = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_duplicate = set()\n",
    "co_author_ans = dict()\n",
    "for author in authors:\n",
    "    all_authors = author.split(\";\")\n",
    "    if len(all_authors) == 1:\n",
    "        continue\n",
    "    for i in range(len(all_authors) - 1):\n",
    "        cur_au = all_authors[i]\n",
    "        for j in range(i + 1, len(all_authors)):\n",
    "            tuple_nw = (cur_au, all_authors[j])    \n",
    "            tuple_nw2 = (all_authors[j], cur_au)\n",
    "        if tuple_nw not in no_duplicate and tuple_nw2 not in no_duplicate:\n",
    "            no_duplicate.add(tuple_nw)\n",
    "            co_author_ans[tuple_nw] = 1\n",
    "            \n",
    "        elif tuple_nw in no_duplicate:\n",
    "            co_author_ans[tuple_nw] += 1\n",
    "        else:\n",
    "            co_author_ans[tuple_nw2] += 1\n",
    "print(co_author_ans)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "//as////CC\n"
     ]
    }
   ],
   "source": [
    "\n",
    "urls = \"https://ijournal-service.topeditsci.com//api/v1/journal/search?pageNum=1&pageSize=10&total=0&keywordType=title&keyword={}&ifStart_2019=&ifEnd_2019=&ifStart=&ifEnd=&jcr=&sub=&selfCitingRate=all&compatriotRate=all&isDomestic=&totalReviewRatio=all&categoryId=&recommend=0&selfStart=&selfEnd=&numberStart=&numberEnd=&trrl=&trrr=&order=&orderType=\".format()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      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ECONOMICS\",\"journalCnTitle\":\"\",\"abbrJournal\":\"Appl. Econ.\",\"issn\":\"0003-6846\",\"impactFactor\":\"1.103\",\"jcrJson\":[{\"edition\":\"SSCI\",\"category\":\"ECONOMICS\",\"jrcZone\":\"Q3\"}],\"jcrZone\":2,\"openAccess\":0,\"annualPublication\":420,\"selfCites\":\"0.08220064\",\"numberOfCites\":0.13,\"publicationPeriod\":60,\"impactFactor_2019\":\"1.835\",\"subZone\":0,\"subjectDcac\":\"-\",\"openAccessYear\":0,\"isDomestic\":0,\"totalReviewRatio\":\"0.00\",\"categoryZone\":[\"['49a8434d2f76f41c41cb68d9fdf5f149']\"],\"publisherName\":\"ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD\",\"open_ratio\":\"0.0229062276306371\",\"jci_json\":\"[{\\\"small_sub\\\": \\\"ECONOMICS\\\", \\\"sub\\\": \\\"Q2\\\"}]\",\"history_quote_factor\":\"[{\\\"year\\\": \\\"2020\\\", \\\"factor\\\": \\\"0.68\\\"}, {\\\"year\\\": \\\"2019\\\", \\\"factor\\\": \\\"0.58\\\"}, {\\\"year\\\": \\\"2018\\\", \\\"factor\\\": \\\"0.52\\\"}, {\\\"year\\\": \\\"2017\\\", \\\"factor\\\": \\\"0.47\\\"}]\",\"quote_factor\":\"0.68\",\"journalDb\":\"SSCI\",\"jcr_Json\":\"[{\\\"small_sub\\\": \\\"ECONOMICS\\\", \\\"sub\\\": \\\"Q3\\\"}]\",\"categoryType\":[]},{\"id\":17380,\"journalTitle\":\"APPLIED ECONOMICS JOURNAL\",\"journalCnTitle\":\"\",\"abbrJournal\":null,\"issn\":\"2586-9124\",\"impactFactor\":\"Not Available\",\"jcrJson\":[],\"jcrZone\":0,\"openAccess\":0,\"annualPublication\":0,\"selfCites\":\"\",\"numberOfCites\":0.14,\"publicationPeriod\":0,\"impactFactor_2019\":\"0.000\",\"subZone\":0,\"subjectDcac\":\"-\",\"openAccessYear\":0,\"isDomestic\":0,\"totalReviewRatio\":null,\"categoryZone\":[\"['79fba135d433a54ea776cd8a80aff35a']\"],\"publisherName\":\"KASERSART UNIV, FAC ECONOMICS\",\"open_ratio\":\"0\",\"jci_json\":\"[{\\\"small_sub\\\": \\\"ECONOMICS\\\", \\\"sub\\\": \\\"Q4\\\"}]\",\"history_quote_factor\":\"[{\\\"year\\\": \\\"2020\\\", \\\"factor\\\": \\\"0.05\\\"}, {\\\"year\\\": \\\"2019\\\", \\\"factor\\\": \\\"0.01\\\"}, {\\\"year\\\": \\\"2018\\\", \\\"factor\\\": \\\"0.04\\\"}, {\\\"year\\\": \\\"2017\\\", \\\"factor\\\": \\\"0.04\\\"}]\",\"quote_factor\":\"0.05\",\"journalDb\":\"ESCI\",\"jcr_Json\":\"[]\",\"categoryType\":[]},{\"id\":8948,\"journalTitle\":\"APPLIED ECONOMICS LETTERS\",\"journalCnTitle\":\"\",\"abbrJournal\":\"Appl. Econ. Lett.\",\"issn\":\"1350-4851\",\"impactFactor\":\"0.752\",\"jcrJson\":[{\"edition\":\"SSCI\",\"category\":\"ECONOMICS\",\"jrcZone\":\"Q4\"}],\"jcrZone\":1,\"openAccess\":0,\"annualPublication\":316,\"selfCites\":\"0.12208504\",\"numberOfCites\":0,\"publicationPeriod\":18,\"impactFactor_2019\":\"1.157\",\"subZone\":0,\"subjectDcac\":\"-\",\"openAccessYear\":0,\"isDomestic\":0,\"totalReviewRatio\":\"0.00\",\"categoryZone\":[\"['49a8434d2f76f41c41cb68d9fdf5f149']\"],\"publisherName\":\"ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD\",\"open_ratio\":\"0.00910470409711684\",\"jci_json\":\"[{\\\"small_sub\\\": \\\"ECONOMICS\\\", \\\"sub\\\": \\\"Q3\\\"}]\",\"history_quote_factor\":\"[]\",\"quote_factor\":null,\"journalDb\":\"SSCI\",\"jcr_Json\":\"[]\",\"categoryType\":[]},{\"id\":17386,\"journalTitle\":\"BIO-BASED AND APPLIED ECONOMICS\",\"journalCnTitle\":\"\",\"abbrJournal\":null,\"issn\":\"2280-6180\",\"impactFactor\":\"Not Available\",\"jcrJson\":[],\"jcrZone\":0,\"openAccess\":0,\"annualPublication\":0,\"selfCites\":\"\",\"numberOfCites\":0,\"publicationPeriod\":0,\"impactFactor_2019\":\"0.000\",\"subZone\":0,\"subjectDcac\":\"-\",\"openAccessYear\":0,\"isDomestic\":0,\"totalReviewRatio\":null,\"categoryZone\":[\"['79fba135d433a54ea776cd8a80aff35a']\"],\"publisherName\":\"FIRENZE UNIV PRESS\",\"open_ratio\":\"1\",\"jci_json\":\"[{\\\"small_sub\\\": \\\"ECONOMICS\\\", \\\"sub\\\": \\\"Q3\\\"}]\",\"history_quote_factor\":\"[{\\\"year\\\": \\\"2020\\\", \\\"factor\\\": \\\"0.36\\\"}, {\\\"year\\\": \\\"2019\\\", \\\"factor\\\": \\\"0.44\\\"}, {\\\"year\\\": \\\"2018\\\", \\\"factor\\\": \\\"0.54\\\"}, {\\\"year\\\": \\\"2017\\\", \\\"factor\\\": \\\"0.38\\\"}]\",\"quote_factor\":\"0.36\",\"journalDb\":\"ESCI\",\"jcr_Json\":\"[]\",\"categoryType\":[]},{\"id\":17469,\"journalTitle\":\"INTERNATIONAL REVIEW OF APPLIED ECONOMICS\",\"journalCnTitle\":\"\",\"abbrJournal\":null,\"issn\":\"0269-2171\",\"impactFactor\":\"Not Available\",\"jcrJson\":[],\"jcrZone\":0,\"openAccess\":0,\"annualPublication\":0,\"selfCites\":\"\",\"numberOfCites\":0.01,\"publicationPeriod\":0,\"impactFactor_2019\":\"0.000\",\"subZone\":0,\"subjectDcac\":\"-\",\"openAccessYear\":0,\"isDomestic\":0,\"totalReviewRatio\":null,\"categoryZone\":[\"['79fba135d433a54ea776cd8a80aff35a']\"],\"publisherName\":\"ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD\",\"open_ratio\":\"0.00689655172413793\",\"jci_json\":\"[{\\\"small_sub\\\": \\\"ECONOMICS\\\", \\\"sub\\\": \\\"Q3\\\"}]\",\"history_quote_factor\":\"[{\\\"year\\\": \\\"2020\\\", \\\"factor\\\": \\\"0.53\\\"}, {\\\"year\\\": \\\"2019\\\", \\\"factor\\\": \\\"0.56\\\"}, {\\\"year\\\": \\\"2018\\\", \\\"factor\\\": \\\"0.54\\\"}, {\\\"year\\\": \\\"2017\\\", \\\"factor\\\": \\\"0.48\\\"}]\",\"quote_factor\":\"0.53\",\"journalDb\":\"ESCI\",\"jcr_Json\":\"[]\",\"categoryType\":[]},{\"id\":18306,\"journalTitle\":\"JOURNAL OF AGRICULTURAL AND APPLIED ECONOMICS\",\"journalCnTitle\":\"\",\"abbrJournal\":null,\"issn\":\"1074-0708\",\"impactFactor\":\"Not Available\",\"jcrJson\":[],\"jcrZone\":0,\"openAccess\":0,\"annualPublication\":0,\"selfCites\":\"\",\"numberOfCites\":0.03,\"publicationPeriod\":0,\"impactFactor_2019\":\"0.000\",\"subZone\":0,\"subjectDcac\":\"-\",\"openAccessYear\":0,\"isDomestic\":0,\"totalReviewRatio\":null,\"categoryZone\":[\"['2fc484016d90ab19742f19878e898a80']\"],\"publisherName\":\"CAMBRIDGE UNIV PRESS\",\"open_ratio\":\"1\",\"jci_json\":\"[{\\\"small_sub\\\": \\\"AGRICULTURAL ECONOMICS & POLICY\\\", \\\"sub\\\": \\\"Q2\\\"}]\",\"history_quote_factor\":\"[{\\\"year\\\": \\\"2020\\\", \\\"factor\\\": \\\"0.75\\\"}, {\\\"year\\\": \\\"2019\\\", \\\"factor\\\": \\\"0.69\\\"}, {\\\"year\\\": \\\"2018\\\", \\\"factor\\\": \\\"0.53\\\"}, {\\\"year\\\": \\\"2017\\\", \\\"factor\\\": \\\"0.61\\\"}]\",\"quote_factor\":\"0.75\",\"journalDb\":\"ESCI\",\"jcr_Json\":\"[]\",\"categoryType\":[]},{\"id\":8300,\"journalTitle\":\"Journal of Applied Economics\",\"journalCnTitle\":\"\",\"abbrJournal\":\"J. Appl. Econ.\",\"issn\":\"1514-0326\",\"impactFactor\":\"0.586\",\"jcrJson\":[{\"edition\":\"SSCI\",\"category\":\"ECONOMICS\",\"jrcZone\":\"Q4\"}],\"jcrZone\":1,\"openAccess\":1,\"annualPublication\":27,\"selfCites\":\"0.04545454\",\"numberOfCites\":0.13,\"publicationPeriod\":2,\"impactFactor_2019\":\"1.100\",\"subZone\":0,\"subjectDcac\":\"-\",\"openAccessYear\":2018,\"isDomestic\":0,\"totalReviewRatio\":\"0.00\",\"categoryZone\":[\"['49a8434d2f76f41c41cb68d9fdf5f149']\"],\"publisherName\":\"ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD\",\"open_ratio\":\"1\",\"jci_json\":\"[{\\\"small_sub\\\": \\\"ECONOMICS\\\", \\\"sub\\\": \\\"Q3\\\"}]\",\"history_quote_factor\":\"[{\\\"year\\\": \\\"2020\\\", \\\"factor\\\": \\\"0.41\\\"}, {\\\"year\\\": \\\"2019\\\", \\\"factor\\\": \\\"0.3\\\"}, {\\\"year\\\": \\\"2018\\\", \\\"factor\\\": \\\"0.32\\\"}, {\\\"year\\\": \\\"2017\\\", \\\"factor\\\": \\\"0.27\\\"}]\",\"quote_factor\":\"0.41\",\"journalDb\":\"SSCI\",\"jcr_Json\":\"[{\\\"small_sub\\\": \\\"ECONOMICS\\\", \\\"sub\\\": \\\"Q4\\\"}]\",\"categoryType\":[]},{\"id\":17473,\"journalTitle\":\"JOURNAL OF APPLIED ECONOMICS AND BUSINESS RESEARCH\",\"journalCnTitle\":\"\",\"abbrJournal\":null,\"issn\":\"1927-033X\",\"impactFactor\":\"Not Available\",\"jcrJson\":[],\"jcrZone\":0,\"openAccess\":0,\"annualPublication\":0,\"selfCites\":\"\",\"numberOfCites\":0.04,\"publicationPeriod\":0,\"impactFactor_2019\":\"0.000\",\"subZone\":0,\"subjectDcac\":\"-\",\"openAccessYear\":0,\"isDomestic\":0,\"totalReviewRatio\":null,\"categoryZone\":[\"['79fba135d433a54ea776cd8a80aff35a']\"],\"publisherName\":\"JOURNAL APPLIED ECONOMICS & BUSINESS RESEARCH\",\"open_ratio\":\"0\",\"jci_json\":\"[{\\\"small_sub\\\": \\\"ECONOMICS\\\", \\\"sub\\\": \\\"Q4\\\"}]\",\"history_quote_factor\":\"[{\\\"year\\\": \\\"2020\\\", \\\"factor\\\": \\\"0.21\\\"}, {\\\"year\\\": \\\"2019\\\", \\\"factor\\\": \\\"0.16\\\"}, {\\\"year\\\": \\\"2018\\\", \\\"factor\\\": \\\"0.13\\\"}, {\\\"year\\\": \\\"2017\\\", \\\"factor\\\": \\\"0.11\\\"}]\",\"quote_factor\":\"0.21\",\"journalDb\":\"ESCI\",\"jcr_Json\":\"[]\",\"categoryType\":[]}]}}\n"
     ]
    }
   ],
   "source": [
    "# df[\"Source_Title\"]\n",
    "\n",
    "import requests\n",
    "import json\n",
    "Titles_detail = {}\n",
    "Titles_input = []\n",
    "for titles in list(df[\"Source_Title\"]):\n",
    "    if titles not in Titles_detail:\n",
    "        Titles_detail[titles] = \"\"\n",
    "        Titles_input.append(\"+\".join(titles.split(\" \")))\n",
    "# test = \"APPLIED+ECONOMICS\"\n",
    "# url = \"https://ijournal-service.topeditsci.com//api/v1/journal/search?pageNum=1&pageSize=10&total=0&keywordType=title&keyword={}&ifStart_2019=&ifEnd_2019=&ifStart=&ifEnd=&jcr=&sub=&selfCitingRate=all&compatriotRate=all&isDomestic=&totalReviewRatio=all&categoryId=&recommend=0&selfStart=&selfEnd=&numberStart=&numberEnd=&trrl=&trrr=&order=&orderType=\".format(Titles_input[0])\n",
    "# url = \"https://ijournal-service.topeditsci.com//api/v1/journal/search?pageNum=1&pageSize=10&total=0&keywordType=title&keyword={}&ifStart_2019=&ifEnd_2019=&ifStart=&ifEnd=&jcr=&sub=&selfCitingRate=all&compatriotRate=all&isDomestic=&totalReviewRatio=all&categoryId=&recommend=0&selfStart=&selfEnd=&numberStart=&numberEnd=&trrl=&trrr=&order=&orderType=\".format(test)\n",
    "\n",
    "# contents = requests.get(url)\n",
    "# print(contents.text)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import time\n",
    "import re\n",
    "import requests\n",
    "import pandas as pd \n",
    "df = pd.read_excel(\"./信息组织-分类作业整理.xlsx\", sheet_name = \"Sheet6\")\n",
    "df.index = df['id']\n",
    "del df['id']\n",
    "\n",
    "Titles_detail = {}\n",
    "Titles_input = []\n",
    "ans = {}\n",
    "for titles in list(set(df[\"Source_Title\"]) ):\n",
    "    if titles not in Titles_detail:\n",
    "        Titles_detail[titles] = \"\"\n",
    "        Titles_input.append(\"+\".join(titles.split(\" \")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n",
      "Not Found! Title:HELIYON\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:JOURNAL OF INTELLIGENT & FUZZY SYSTEMS\n",
      "Done\n",
      "Not Found! Title:REVIEW OF ECONOMIC ANALYSIS\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:CONTEMPORARY ECONOMICS\n",
      "Done\n",
      "Not Found! Title:OPERATIONS AND SUPPLY CHAIN MANAGEMENT-AN INTERNATIONAL JOURNAL\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:JOURNAL OF INNOVATION ECONOMICS & MANAGEMENT\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:ACM JOURNAL OF DATA AND INFORMATION QUALITY\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "None!\n",
      "Not Found! Title:JAMIA OPEN\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS\n",
      "None!\n",
      "Not Found! Title:BLOCKCHAIN TECHNOLOGY: PLATFORMS, TOOLS AND USE CASES\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:JOURNAL OF BEHAVIORAL AND EXPERIMENTAL FINANCE\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:JOURNAL OF INTELLIGENT SYSTEMS\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:REVIEW OF CORPORATE FINANCE STUDIES\n",
      "Not Found! Title:JOURNAL OF RISK AND FINANCIAL MANAGEMENT\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:STRATEGIC CHANGE-BRIEFINGS IN ENTREPRENEURIAL FINANCE\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:INTERNET OF THINGS\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:FUTURE INTERNET\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:EUROPEAN JOURNAL OF INVESTIGATION IN HEALTH PSYCHOLOGY AND EDUCATION\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:JOURNAL OF FINANCIAL REGULATION\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "None!\n",
      "Not Found! Title:CUREUS JOURNAL OF MEDICAL SCIENCE\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:JOURNAL OF PRIMARY CARE AND COMMUNITY HEALTH\n",
      "Not Found! Title:INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:CRIME SCIENCE\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:COGNITIVE COMPUTATION\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "None!\n",
      "Not Found! Title:FRONTIERS IN BLOCKCHAIN\n",
      "Done\n",
      "None!\n",
      "Not Found! Title:ROLE OF BLOCKCHAIN TECHNOLOGY IN IOT APPLICATIONS\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:IDP-INTERNET LAW AND POLITICS\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:COMPUTERS & SECURITY\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:AXIOMS\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Not Found! Title:JOURNAL OF INVESTING\n",
      "Done\n",
      "Not Found! Title:INFORMATION\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "Done\n",
      "None!\n",
      "Not Found! Title:ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, APMS 2021, PT I\n",
      "Done\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import time\n",
    "import re\n",
    "import requests\n",
    "import pandas as pd \n",
    "df = pd.read_excel(\"./分类作业整理.xlsx\", sheet_name = \"Sheet6\")\n",
    "df.index = df['id']\n",
    "del df['id']\n",
    "\n",
    "Titles_detail = {}\n",
    "Titles_input = []\n",
    "ans = {}\n",
    "for titles in list(set(df[\"Source_Title\"]) ):\n",
    "    if titles not in Titles_detail:\n",
    "        Titles_detail[titles] = \"\"\n",
    "        Titles_input.append(\"+\".join(titles.split(\" \")))\n",
    "for t in list(set(df[\"Source_Title\"]) ):\n",
    "    ans[t] = []\n",
    "\n",
    "for title in Titles_input:\n",
    "    url = \"https://ijournal-service.topeditsci.com//api/v1/journal/ \\\n",
    "        search?pageNum=1&pageSize=10&total=0&keywordType=title&keyword={}&ifStart_2019=&ifEnd_2019=\\\n",
    "        &ifStart=&ifEnd=&jcr=&sub=&selfCitingRate=all&compatriotRate=all&isDomestic=&totalReviewRatio=all\\\n",
    "        &categoryId=&recommend=0&selfStart=&selfEnd=&numberStart=&numberEnd=&trrl=&trrr=&order=&orderType=\".format(title)\n",
    "    contents = requests.get(url)\n",
    "    ipt = json.loads(contents.text)\n",
    "    found = False\n",
    "    pt = \" \".join(title.split(\"+\"))\n",
    "    if len(ipt[\"data_original\"]['data']) == 0:\n",
    "        print(\"None!\")\n",
    "    else:\n",
    "        for i in range(len(ipt[\"data_original\"]['data'])):\n",
    "            if ipt[\"data_original\"]['data'][i]['journalTitle'].lower() == pt.lower() and float(ipt[\"data_original\"]['data'][i]['impactFactor_2019']) > 0:\n",
    "                try:\n",
    "                    ans[pt].append(ipt[\"data_original\"]['data'][i]['impactFactor_2019'])\n",
    "                except Exception:\n",
    "                    ans[pt].append(\"【None】\")\n",
    "                try:\n",
    "                    ans[pt].append(ipt[\"data_original\"]['data'][i]['jcrJson'][0]['jrcZone'])\n",
    "                except Exception:\n",
    "                    ans[pt].append(\"【None】\")\n",
    "                try:\n",
    "                    ans[pt].append(str(ipt[\"data_original\"]['data'][i]['annualPublication']))\n",
    "                except Exception:\n",
    "                    ans[pt].append(\"【None】\")\n",
    "                try:\n",
    "                    ans[pt].append(re.findall(r'\"sub\": \"(.*?)\"',  ipt[\"data_original\"]['data'][i]['jci_json'][1:-1])[0]) #JCI\n",
    "                except Exception:\n",
    "                    ans[pt].append(\"【None】\")\n",
    "                try:\n",
    "                    ans[pt].append(ipt[\"data_original\"]['data'][i]['quote_factor']) \n",
    "                except Exception:\n",
    "                    ans[pt].append(\"【None】\")\n",
    "                found = True\n",
    "                break\n",
    "    if not found:\n",
    "        print(\"Not Found! Title:{}\".format(pt))\n",
    "    else:\n",
    "        print(\"Done\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "uuu = ', SOFTWARE ENGINEERING\", \"sub\": \"Q1\"}, {\"small_sub\": \"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\", \"sub\": \"Q1\" '\n",
    "t = re.findall(r'\"sub\": \"(.*?)\"', uuu)[0]\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 30869,\n",
       " 'journalTitle': 'Environmental Technology (United Kingdom)',\n",
       " 'journalCnTitle': '',\n",
       " 'abbrJournal': None,\n",
       " 'issn': '9593330',\n",
       " 'impactFactor': None,\n",
       " 'jcrJson': [],\n",
       " 'jcrZone': 0,\n",
       " 'openAccess': 0,\n",
       " 'annualPublication': 0,\n",
       " 'selfCites': '',\n",
       " 'numberOfCites': 0,\n",
       " 'publicationPeriod': 0,\n",
       " 'impactFactor_2019': '0.000',\n",
       " 'subZone': -1,\n",
       " 'subjectDcac': '',\n",
       " 'openAccessYear': 0,\n",
       " 'isDomestic': 0,\n",
       " 'totalReviewRatio': None,\n",
       " 'categoryZone': ['4b68f6cccd94d1f983a0560db13c6756',\n",
       "  'eb36d5928bc3ca291d51eb4b50098892',\n",
       "  '4b68f6cccd94d1f983a0560db13c6755'],\n",
       " 'publisherName': 'Taylor and Francis Ltd.',\n",
       " 'open_ratio': '-',\n",
       " 'jci_json': None,\n",
       " 'history_quote_factor': None,\n",
       " 'quote_factor': None,\n",
       " 'journalDb': 'EI',\n",
       " 'jcr_Json': None,\n",
       " 'categoryType': ['EI']}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "Factors = []\n",
    "JRCZone = []\n",
    "Annual_publications = []\n",
    "JCLZone = []\n",
    "Quote_ratio = []\n",
    "Sources = []\n",
    "tt = 0\n",
    "for key, values in ans.items():\n",
    "    Sources.append(key)\n",
    "    if len(values) < 5:\n",
    "        Factors.append(\"【MISS】\")\n",
    "        JRCZone.append(\"【MISS】\")\n",
    "        Annual_publications.append(\"【MISS】\")\n",
    "        JCLZone.append(\"【MISS】\")\n",
    "        Quote_ratio.append(\"【MISS】\")\n",
    "    else:\n",
    "        Factors.append(values[0])\n",
    "        JRCZone.append(values[1])\n",
    "        Annual_publications.append(values[2])\n",
    "        JCLZone.append(values[3])\n",
    "        Quote_ratio.append(values[4])\n",
    "\n",
    "out = pd.DataFrame({\"Source\" : Sources, \"Factors\" : Factors, \"AnnualPublications\" :Annual_publications, \"JRCZone\" : JRCZone,\"JCLZone\": JCLZone, \"Quote_factor\": Quote_ratio })\n",
    "out.to_csv(\"./test4.csv\", index = None)\n",
    "\n",
    "# print(tt)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# addresses = list(df[\"Addresses\"])\n",
    "# print(len((addresses)))\n",
    "# print(addresses[8])\n",
    "import pandas as pd \n",
    "df = pd.read_excel(\"./信息组织-分类作业整理.xlsx\", sheet_name = \"Sheet6\")\n",
    "df.index = df['id']\n",
    "del df['id']\n",
    "df.head()   \n",
    "addresses = list(df[\"Addresses\"])\n",
    "addresses[8] = \"[Kim, Thomas] Univ Calif Riverside, Sch Business Adm, Riverside, CA 92521 USA\"\n",
    "addresses[1939] = \"[Wang, Hao; Liu, Zhe; Ge, Chunpeng] Nanjing Univ Aeronaut & Astronaut, Nanjing, Peoples R China; [Sakurai, Kouichi] Kyushu Univ, Fukuoka 8190395, Japan; [Su Chunhua] Univ Aizu, Aizu Wakamatsu, Fukushima 9650107, Japan\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# addresses[1939] = \"[Wang, Hao;Liu, Zhe;Ge, Chunpeng] Nanjing Univ Aeronaut & Astronaut, Nanjing, Peoples R China;[Sakurai, Kouichi] Kyushu Univ, Fukuoka 8190395, Japan; [Su Chunhua] Univ Aizu, Aizu Wakamatsu, Fukushima 9650107, Japan\"\n",
    "\n",
    "import re\n",
    "result = []\n",
    "for index, addre in enumerate(addresses):\n",
    "    if index == 735 or index == 1799:\n",
    "        continue    \n",
    "    groups = re.findall(r'\\[(.*?)\\]', addre) \n",
    "    for idx, group in enumerate(groups):\n",
    "        if \"; \" not in group:\n",
    "            groups[idx] = [group]\n",
    "        else:\n",
    "            groups[idx] = group.split(\";\")\n",
    "    # print(groups)\n",
    "    addre = re.sub(r'\\[(.*?)\\]', \"\", addre)\n",
    "    addre = addre.split(\"; \")\n",
    "    for idx, aut in enumerate(groups):\n",
    "        for a in aut:\n",
    "            result.append((index + 1, a, addre[idx]))\n",
    "    # if len(groups) != len(addre.split(\"; \")):\n",
    "    #     print(\"O\")\n",
    "    #     print(addre)\n",
    "\n",
    "    \n",
    "# 注：编号736 跳过\n",
    "\n",
    "# 编号1800: 有个人对应多个组织\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[Wang, Hao] Nanjing Univ Aeronaut & Astronaut, Nanjing, Peoples R China; Kyushu Univ, Fukuoka 8190395, Japan; Univ Aizu, Aizu Wakamatsu, Fukushima 9650107, Japan'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addresses[1939] = \"[Wang, Hao; Liu, Zhe; Ge, Chunpeng] Nanjing Univ Aeronaut & Astronaut, Nanjing, Peoples R China;[Sakurai, Kouichi] Kyushu Univ, Fukuoka 8190395, Japan; [Su, Chunhua] Univ Aizu, Aizu Wakamatsu, Fukushima 9650107, Japan\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hungary 28\n",
      "Switzerland 76\n",
      "Australia 396\n",
      "United States 802\n",
      "Austria 31\n",
      "Spain 148\n",
      "United Kindom 523\n",
      "Czech Republic 22\n",
      "Norway 102\n",
      "China 3261\n",
      "Canada 231\n",
      "Belgium 19\n",
      "Slovakia 8\n",
      "Germany 251\n",
      "Poland 65\n",
      "Ireland 106\n",
      "South Korea 403\n",
      "South Africa 25\n",
      "Italy 259\n",
      "Morocco 11\n",
      "Japan 101\n",
      "Greece 70\n",
      "Lebanon 38\n",
      "France 171\n",
      "Singapore 134\n",
      "Brazil 71\n",
      "Turkey 82\n",
      "Netherlands 64\n",
      "Argentina 9\n",
      "Peru 1\n",
      "Estonia 2\n",
      "Denmark 73\n",
      "India 519\n",
      "Saudi Arabia 225\n",
      "Taiwan 149\n",
      "Ecuador 8\n",
      "U Arab Emirates 199\n",
      "Zambia 3\n",
      "Slovenia 31\n",
      "Sweden 31\n",
      "Finland 59\n",
      "Chile 7\n",
      "Russia 30\n",
      "Iran 24\n",
      "Israel 5\n",
      "Tunisia 32\n",
      "Oman 10\n",
      "North Ireland 4\n",
      "Ukraine 5\n",
      "New Zealand 52\n",
      "Pakistan 212\n",
      "Bangladesh 22\n",
      "Palestine 2\n",
      "Botswana 1\n",
      "Vietnam 67\n",
      "Thailand 15\n",
      "Croatia 7\n",
      "Qatar 57\n",
      "Malaysia 73\n",
      "Ghana 6\n",
      "Romania 45\n",
      "Indonesia 4\n",
      "Algeria 21\n",
      "Philippines 3\n",
      "Mexico 19\n",
      "Kenya 1\n",
      "Luxembourg 6\n",
      "Portugal 74\n",
      "Sri Lanka 10\n",
      "Egypt 31\n",
      "Malta 2\n",
      "Jordan 19\n",
      "Iraq 1\n",
      "Kazakhstan 16\n",
      "Azerbaijan 1\n",
      "Colombia 17\n",
      "Cyprus 2\n",
      "Kuwait 5\n",
      "Mauritius 3\n",
      "Serbia 11\n",
      "Papua N Guinea 1\n",
      "Yemen 3\n",
      "Lithuania 1\n",
      "Rwanda 1\n",
      "Cuba 3\n",
      "Iceland 3\n",
      "Nigeria 2\n",
      "Angola 1\n",
      "Bulgaria 2\n",
      "Republic 1\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "out_1 = []\n",
    "out_2 = []\n",
    "out_3 = []\n",
    "out_4 = []\n",
    "for res_ in result:\n",
    "    out_1.append(res_[0])\n",
    "    out_2.append(res_[1])\n",
    "    out_3.append(res_[2])\n",
    "\n",
    "for i in out_3:\n",
    "    if \"USA\" in i:\n",
    "        out_4.append(\"United States\")\n",
    "    elif \"Wales\" in i or \"England\" in i or \"Scotland\" in i:\n",
    "        out_4.append(\"United Kindom\")\n",
    "    elif \"Peoples R China\" in i :\n",
    "        out_4.append(\"China\")\n",
    "    else:\n",
    "        out_4.append(i.split(\",\")[-1].strip() )\n",
    "\n",
    "# print(Counter(out_4))\n",
    "out_4 = dict(Counter(out_4))\n",
    "for key, value in out_4.items():\n",
    "    print(key, value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Map\n",
    "from pyecharts.faker import Faker\n",
    "\n",
    "c = (\n",
    "    Map()\n",
    "    .add( \"\",\n",
    "    [(key, value) for key, value in out_4.items()], \n",
    "    \"world\",\n",
    "    is_map_symbol_show=False,\n",
    "    )\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"作者数量\", pos_left=\"45%\"),\n",
    "        visualmap_opts=opts.VisualMapOpts(\n",
    "                max_ = 3500,\n",
    "                split_number=6,\n",
    "                is_piecewise=True,\n",
    "                pos_left =90,\n",
    "                pos_bottom=40,\n",
    "                pieces=[\n",
    "                  {\"min\": 0, \"max\": 10, \"label\": '0 - 10'},\n",
    "                  {\"min\": 10, \"max\": 30, \"label\": '10 - 30'},\n",
    "                  {\"min\": 30, \"max\": 100, \"label\": '30 - 100'},\n",
    "                  {\"min\": 100, \"max\": 500, \"label\": '100 - 500'},\n",
    "                  {\"min\": 500, \"max\": 1000, \"label\": '500 - 1000'},\n",
    "                  {\"min\": 1000, \"label\": ' > 1000'},\n",
    "\n",
    "    ],\n",
    "        )\n",
    "    )\n",
    "    .render(\"map_world.html\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'China': 3261, 'United States': 802, 'United Kindom': 523, 'India': 519, 'South Korea': 403, 'Australia': 396, 'Italy': 259, 'Germany': 251, 'Canada': 231, 'Saudi Arabia': 225, 'Pakistan': 212, 'U Arab Emirates': 199, 'France': 171, 'Taiwan': 149, 'Spain': 148, 'Singapore': 134, 'Ireland': 106, 'Norway': 102, 'Japan': 101, 'Turkey': 82, 'Switzerland': 76, 'Portugal': 74, 'Denmark': 73, 'Malaysia': 73, 'Brazil': 71, 'Greece': 70, 'Vietnam': 67, 'Poland': 65, 'Netherlands': 64, 'Finland': 59, 'Qatar': 57, 'New Zealand': 52, 'Romania': 45, 'Lebanon': 38, 'Tunisia': 32, 'Austria': 31, 'Slovenia': 31, 'Sweden': 31, 'Egypt': 31, 'Russia': 30, 'Hungary': 28, 'South Africa': 25, 'Iran': 24, 'Czech Republic': 22, 'Bangladesh': 22, 'Algeria': 21, 'Belgium': 19, 'Mexico': 19, 'Jordan': 19, 'Colombia': 17, 'Kazakhstan': 16, 'Thailand': 15, 'Morocco': 11, 'Serbia': 11, 'Oman': 10, 'Sri Lanka': 10, 'Argentina': 9, 'Slovakia': 8, 'Ecuador': 8, 'Chile': 7, 'Croatia': 7, 'Ghana': 6, 'Luxembourg': 6, 'Israel': 5, 'Ukraine': 5, 'Kuwait': 5, 'North Ireland': 4, 'Indonesia': 4, 'Zambia': 3, 'Philippines': 3, 'Mauritius': 3, 'Yemen': 3, 'Cuba': 3, 'Iceland': 3, 'Estonia': 2, 'Palestine': 2, 'Malta': 2, 'Cyprus': 2, 'Nigeria': 2, 'Bulgaria': 2, 'Peru': 1, 'Botswana': 1, 'Kenya': 1, 'Iraq': 1, 'Azerbaijan': 1, 'Papua N Guinea': 1, 'Lithuania': 1, 'Rwanda': 1, 'Angola': 1, 'Republic': 1})\n"
     ]
    }
   ],
   "source": [
    "# dfff = pd.read_excel(\"output_with_impactFactor.xlsx\", sheet_name=\"作者地址详细信息\")\n",
    "# # dfff['Authors'] = dfff['Authors'].dropna()\n",
    "# authors__ = list(dfff['Authors'])[:5809]\n",
    "# # print(authors__)\n",
    "# authors__ = list(map(lambda x : (x.replace(\",\", \"\") ).lower(), authors__))\n",
    "# tud = pd.DataFrame({\"Target\" : authors__})\n",
    "# tud.to_excel(\"test2.xlsx\", sheet_name=\"all_authors\")\n",
    "print(Counter(out_4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 下面做一下国际分析,每个文章是哪些国家合作的结果\n",
    "import pandas as pd \n",
    "df_main = pd.read_excel(\"./信息组织-分类作业整理.xlsx\", sheet_name = \"Sheet6\")\n",
    "df_main.index = df_main['id']\n",
    "del df_main['id']\n",
    "df_main.head()   \n",
    "# addresses = list(df_main[\"Addresses\"])\n",
    "# addresses[8] = \"[Kim, Thomas] Univ Calif Riverside, Sch Business Adm, Riverside, CA 92521 USA\"\n",
    "# addresses[1939] = \"[Wang, Hao; Liu, Zhe; Ge, Chunpeng] Nanjing Univ Aeronaut & Astronaut, Nanjing, Peoples R China; [Sakurai, Kouichi] Kyushu Univ, Fukuoka 8190395, Japan; [Su Chunhua] Univ Aizu, Aizu Wakamatsu, Fukushima 9650107, Japan\"\n",
    "\n",
    "\n",
    "# df = df_main[df_main[\"Publication_Year\"].isin([2019,2020,2021,2022])]\n",
    "df = df_main[df_main[\"Publication_Year\"].isin([2014,2015,2016,2017,2018])]\n",
    "# print(df[\"Publication_Year\"])\n",
    "\n",
    "co_nations = []\n",
    "nations_ = list(df[\"Addresses\"])\n",
    "nations_ = map(lambda x : re.sub(r'\\[(.*?)\\]', \"\", x), nations_)\n",
    "\n",
    "for index, nations in enumerate(nations_):\n",
    "    temp_nations = nations.split(\"; \")\n",
    "    current_add = []\n",
    "    for m in temp_nations:\n",
    "        \n",
    "        m_temp = m.split(\",\")[-1]\n",
    "        if \"USA\" in m_temp:\n",
    "            if \"United States\" not in current_add:\n",
    "                current_add.append(\"United States\")\n",
    "        elif (\"Wales\" in m_temp or \"England\" in m_temp or \"Scotland\" in m_temp):\n",
    "            if \"United Kingdom\" not in current_add :\n",
    "                current_add.append(\"United Kingdom\")\n",
    "        elif \"Peoples R China\" in m_temp :\n",
    "            if \"China\" not in current_add:\n",
    "                current_add.append(\"China\")\n",
    "        elif m_temp.strip() not in current_add :\n",
    "            current_add.append(m_temp.strip() )\n",
    "    co_nations.append(current_add)\n",
    "\n",
    "        \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Japan,Lebanon,Norway,Ireland,Singapore,Spain,Canada,France,Germany,Italy,Australia,South Korea,United Kingdom,United States,China\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]\n",
      "[0, 0, 2, 1, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 1]\n",
      "[0, 2, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 3]\n",
      "[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 1, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 3, 2, 5]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0]\n",
      "[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 6]\n",
      "[0, 6, 2, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 5, 2]\n",
      "[0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 3, 0, 3, 3, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 2]\n",
      "[0, 0, 0, 1, 0, 0, 0, 1, 3, 1, 0, 1, 0, 2, 3]\n",
      "[0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 2]\n",
      "[0, 0, 0, 2, 3, 1, 0, 1, 3, 0, 0, 0, 0, 3, 2]\n",
      "[1, 0, 0, 1, 2, 1, 3, 5, 3, 0, 2, 1, 3, 0, 10]\n",
      "[0, 1, 3, 0, 5, 0, 6, 2, 0, 2, 3, 2, 2, 10, 0]\n"
     ]
    }
   ],
   "source": [
    "# {'China': 3261, 'United States': 802, \n",
    "# 'United Kindom': 523, 'India': 519, \n",
    "# 'South Korea': 403, 'Australia': 396, \n",
    "# 'Italy': 259, 'Germany': 251, 'Canada': 231, \n",
    "# 'Saudi Arabia': 225, 'Pakistan': 212, 'U Arab Emirates': \n",
    "# 199, 'France': 171, 'Taiwan': 149, 'Spain': 148, 'Singapore': 134, \n",
    "# 'Ireland': 106, 'Norway': 102, 'Japan': 101, 'Turkey': 82, 'Switzerland': 76,\n",
    "#  'Portugal': 74, 'Denmark': 73, 'Malaysia': 73, 'Brazil': 71, 'Greece': 70, 'Vietnam': 67, \n",
    "# 'Poland': 65, 'Netherlands': 64, 'Finland': 59, 'Qatar': 57, 'New Zealand': 52, 'Romania': 45, 'Lebanon': 38, \n",
    "\n",
    "# 主要研究前10密度国家的合作情况\n",
    "# \n",
    "# \"United Kingdom\" in uni\n",
    "# China \\ US \\ UK \\ South  Korea \\ Australia 、 Italy 、 Germany \\ \n",
    "\n",
    "uni = set()\n",
    "for t in co_nations:\n",
    "    for m in t:\n",
    "        uni.add(m)\n",
    "# print(len(uni))\n",
    "\n",
    "def get_in(target, arr):\n",
    "    return len(list(filter(lambda x : target in x, arr)))\n",
    "\n",
    "def get_only(target, arr):\n",
    "    return len(list(filter(lambda x : target in x and len(x) == 1, arr)))\n",
    "\n",
    "def get_both(target1, target2 ,arr):\n",
    "    if target1 == target2:\n",
    "        print(\"Wrong!\")\n",
    "        pass\n",
    "    return len(list(filter(lambda x : target1 in x and target2 in x, arr)))\n",
    "\n",
    "\n",
    "essays = [(nation, get_in(nation, co_nations) ) for nation in uni]\n",
    "essays = sorted( essays, key = lambda x :x[1])\n",
    "# essays[-15:]\n",
    "\n",
    "NATIONS = [i[0] for i in essays[-15:]]\n",
    "# print(NATIONS)\n",
    "\n",
    "matrix =[]\n",
    "for i in range(15):\n",
    "    matrix.append([0] * 15)\n",
    "\n",
    "for i in range(15):\n",
    "    for j in range(i + 1, 15):\n",
    "        matrix[i][j] = get_both(NATIONS[i], NATIONS[j], co_nations)\n",
    "        matrix[j][i] = matrix[i][j]\n",
    "        \n",
    "# print(matrix)\n",
    "# for i in NATIONS:\n",
    "print(\",\".join(NATIONS))\n",
    "    # print(i)\n",
    "for i in matrix:\n",
    "    print(i)\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "A = np.array(matrix)\n",
    "data = pd.DataFrame(A)\n",
    "\n",
    "writer = pd.ExcelWriter('text5.xlsx')\t\t# 写入Excel文件\n",
    "data.to_excel(writer, 'before_2019')\t\t# ‘page_1’是写入excel的sheet名\n",
    "writer.save()\n",
    "\n",
    "writer.close()\n",
    "# for nation in NATIONS:\n",
    "#     print(nation + \" \\tIN: {}; \\tONLY:{}\".format( str(get_in(nation , co_nations)), str(get_only(nation, co_nations))  ))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "## Import \n",
    "\n",
    "\n",
    "df = pd.read_excel(\"text4.xlsx\", sheet_name = \"before_2019\")\n",
    "df2 = pd.read_excel(\"text4.xlsx\", sheet_name = \"after_2019\")\n",
    "\n",
    "# Read Data\n",
    "\n",
    "d = df.corr()\n",
    "d2 = df2.corr()\n",
    "\n",
    "# Corr matrix\n",
    "\n",
    "# Plot!\n",
    "plt.figure(figsize= (10, 16))\n",
    "plt.subplot(2,1,1)\n",
    "sns.heatmap(d2,annot = True,vmax = 1,square = True,cmap = \"Reds\")\n",
    "plt.xticks(rotation=38)\n",
    "plt.title(\"National Cooperation Before 2019\")\n",
    "plt.subplot(2,1,2)\n",
    "sns.heatmap(d,annot = True,vmax = 1,square = True,cmap = \"Reds\")\n",
    "plt.xticks(rotation=38)\n",
    "plt.title(\"National Cooperation After 2019\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "244"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 优先选择**这个👇\n",
    "\n",
    "# https://www.webofscience.com/wos/alldb/summary/9450f082-8ed8-45f0-a0ce-eb56dcd3deef-3ffdb115/relevance/1 **这个\n",
    "# https://www.webofscience.com/wos/alldb/summary/9594621a-6420-4f21-9f9f-bfa0edfc8a5b-3ffdaa59/relevance/1\n",
    "\n",
    "# https://www.webofscience.com/wos/woscc/cited-references-summary/WOS:000747122600008?type=colluid&from=woscc\n",
    "# https://www.webofscience.com/wos/woscc/cited-references-summary/WOS:000682147600036?type=colluid&from=woscc\n",
    "\n",
    "# WOS:000682147600036\n",
    "# href=\"/wos/woscc/cited-references-summary/WOS:000747122600008?type=colluid&from=woscc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_main = pd.read_excel(\"data.xlsx\", sheet_name = \"data\") #读数据\n",
    "DOI = list(df_main[\"DOI\"])\n",
    "DOI = list(map(lambda x : str(x), DOI))\n",
    "print(\" \".join(DOI))\n"
   ]
  }
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